Error processing and inhibitory control enable the adjustment of behaviors to meet task demands. Functional magnetic resonance imaging studies report brain activation abnormalities in patients with ...obsessive-compulsive disorder (OCD) during both processes. However, conclusions are limited by inconsistencies in the literature and small sample sizes. Therefore, the aim here was to perform a meta-analysis of the existing literature using unthresholded statistical maps from previous studies.
A voxelwise seed-based d mapping meta-analysis was performed using t-maps from studies comparing patients with OCD and healthy control subjects (HCs) during error processing and inhibitory control. For the error processing analysis, 239 patients with OCD (120 male; 79 medicated) and 229 HCs (129 male) were included, while the inhibitory control analysis included 245 patients with OCD (120 male; 91 medicated) and 239 HCs (135 male).
Patients with OCD, relative to HCs, showed longer inhibitory control reaction time (standardized mean difference = 0.20, p = .03, 95% confidence interval = 0.016, 0.393) and more inhibitory control errors (standardized mean difference = 0.22, p = .02, 95% confidence interval = 0.039, 0.399). In the brain, patients showed hyperactivation in the bilateral dorsal anterior cingulate cortex, supplementary motor area, and pre-supplementary motor area as well as right anterior insula/frontal operculum and anterior lateral prefrontal cortex during error processing but showed hypoactivation during inhibitory control in the rostral and ventral anterior cingulate cortices and bilateral thalamus/caudate, as well as the right anterior insula/frontal operculum, supramarginal gyrus, and medial orbitofrontal cortex (all seed-based d mapping z value >2, p < .001).
A hyperactive error processing mechanism in conjunction with impairments in implementing inhibitory control may underlie deficits in stopping unwanted compulsive behaviors in the disorder.
The ability to perform sit-to-stand (STS) transfers has a significant impact on the functional mobility of an individual. Wearable technology has the potential to enable the objective, long-term ...monitoring of STS transfers during daily life. However, despite several recent efforts, most algorithms for detecting STS transfers rely on multiple sensing modalities or device locations and have predominantly been used for assessment during the performance of prescribed tasks in a lab setting. A novel wavelet-based algorithm for detecting STS transfers from data recorded using an accelerometer on the lower back is presented herein. The proposed algorithm is independent of device orientation and was validated on data captured in the lab from younger and older healthy adults as well as in people with Parkinson's disease (PwPD). The algorithm was then used for processing data captured in free-living conditions to assess the ability of multiple features extracted from STS transfers to detect age-related group differences and assess the impact of monitoring duration on the reliability of measurements. The results show that performance of the proposed algorithm was comparable or significantly better than that of a commercially available system (precision: 0.990 vs. 0.868 in healthy adults) and a previously published algorithm (precision: 0.988 vs. 0.643 in persons with Parkinson's disease). Moreover, features extracted from STS transfers at home were able to detect age-related group differences at a higher level of significance compared to data captured in the lab during the performance of prescribed tasks. Finally, simulation results showed that a monitoring duration of 3 days was sufficient to achieve good reliability for measurement of STS features. These results point towards the feasibility of using a single accelerometer on the lower back for detection and assessment of STS transfers during daily life. Future work in different patient populations is needed to evaluate the performance of the proposed algorithm, as well as assess the sensitivity and reliability of the STS features.
Technological advances in multimodal wearable and connected devices have enabled the measurement of human movement and physiology in naturalistic settings. The ability to collect continuous activity ...monitoring data with digital devices in real-world environments has opened unprecedented opportunity to establish clinical digital phenotypes across diseases. Many traditional assessments of physical function utilized in clinical trials are limited because they are episodic, therefore, cannot capture the day-to-day temporal fluctuations and longitudinal changes in activity that individuals experience. In order to understand the sensitivity of gait speed as a potential endpoint for clinical trials, we investigated the use of digital devices during traditional clinical assessments and in real-world environments in a group of healthy younger (
= 33, 18-40 years) and older (
= 32, 65-85 years) adults. We observed good agreement between gait speed estimated using a lumbar-mounted accelerometer and gold standard system during the performance of traditional gait assessment task in-lab, and saw discrepancies between in-lab and at-home gait speed. We found that gait speed estimated in-lab, with or without digital devices, failed to differentiate between the age groups, whereas gait speed derived during at-home monitoring was able to distinguish the age groups. Furthermore, we found that only three days of at-home monitoring was sufficient to reliably estimate gait speed in our population, and still capture age-related group differences. Our results suggest that gait speed derived from activities during daily life using data from wearable devices may have the potential to transform clinical trials by non-invasively and unobtrusively providing a more objective and naturalistic measure of functional ability.
Introduction Recent data implicate abnormalities of the thalamic reticular nucleus (TRN) and thalamocortical circuitry in schizophrenia (SZ) risk. Sleep spindles are initiated by the TRN and ...propagated to the cortex via thalamocortical feedback loops.During wakefulness, TRN modulates sensory processing by gating thalamocortical communication. Patients with SZ and their first-degree relatives (familial-high-risk,FHR) exhibit sleep spindle and sensory gating deficits. In two studies, we investigated the two SZ biomarkers in relation to thalamocortical connectivity in chronic and early-course psychotic disorders and FHR. Methods Twenty-six SZ and 29 healthy controls (HC) participated in Study1; 10 early-course patients with psychotic disorders (PSY), 14 FHR and 16 HC participated in Study2. All participants completed a resting-state fMRI session and thalamocortical seed-to-voxel connectivity was computed. In a separate session, nocturnal sleep was monitored with PSG for Study1. Sleep spindles were identified using an automated wavelet detector. Study2 included a sensory gating event-related-potentials (ERP) experiment. Gating was calculated as the suppression of the auditory P50 for the second of a pair of identical clicks. Whole brain regression analyses were used to examine relations of thalamocortical connectivity with spindles and sensory gating (we report pFDR ≤.05). Results SZ showed widespread reductions in spindle density (38 electrodes, pcorrected =.009). Reduced spindle density was associated with significantly greater thalamic connectivity with left sensory-motor cortex (MNI:x=-24,y=-16,z=64; BA4; no slope difference) in regions that overlap with those SZ patients show abnormal thalamocortical hyperconnectivity. Relative to HC, PSY exhibited marginally-reduced sensory gating (t(19)=-1.9, p=.07; FHR vs HC non-significant). Reduced sensory gating correlated with weaker thalamocortical connectivity in the right dorsolateral-prefrontal-cortex (DLPFC;44,44,-2; BA46; no slope difference) connectivity in this cluster was significantly reduced in PSY vs. HC (t(19)= 2.2, p=.04; FHR vs HC non-significant). Conclusion In two experiments, we show that two SZ biomarkers, sleep spindle and sensory gating deficits are associated with abnormal thalamocortical connectivity, suggesting that they arise from a common mechanism. Data collection for Study2 is ongoing and increased sample sizes will allow for analysis of the specificity of these abnormalities to SZ and SZ risk. Support (If Any) K01MH114012
Digital health technologies (DHTs) enable us to measure human physiology and behavior remotely, objectively and continuously. With the accelerated adoption of DHTs in clinical trials, there is an ...unmet need to identify statistical approaches to address missing data to ensure that the derived endpoints are valid, accurate, and reliable. It is not obvious how commonly used statistical methods to handle missing data in clinical trials can be directly applied to the complex data collected by DHTs. Meanwhile, current approaches used to address missing data from DHTs are of limited sophistication and focus on the exclusion of data where the quantity of missing data exceeds a given threshold.
High-frequency time series data collected by DHTs are often summarized to derive epoch-level data, which are then processed to compute daily summary measures. In this article, we discuss characteristics of missing data collected by DHT, review emerging statistical approaches for addressing missingness in epoch-level data including within-patient imputations across common time periods, functional data analysis, and deep learning methods, as well as imputation approaches and robust modeling appropriate for handling missing data in daily summary measures. We discuss strategies for minimizing missing data by optimizing DHT deployment and by including the patients' perspectives in the study design. We believe that these approaches provide more insight into preventing missing data when deriving digital endpoints. We hope this article can serve as a starting point for further discussion among clinical trial stakeholders.
The accurate assessment of dietary behavior in clinical trials, including number and size of meal intakes, is essential to evaluate participants’ metabolic health which, in turn, might reflect an ...increased risk for developing diabetes. Traditional dietary assessments leverage handwritten/digital food logs, which are error-prone and time-consuming for both users and investigators. Instead, glucose timeseries unobtrusively collected by CGM could provide objective measures of meals’ consumption. Here we present a new algorithm to retrospectively identify meal intakes using CGM data only.
In the Geriatric Anorexia Study (NCT04858932), 50 healthy individuals (26 females, mean±SD age: 72.26±5.02 years, BMI: 24.96±3.33 kg/m2) were monitored in free-living conditions for 2 weeks with the Abbott FreeStyle Libre Pro, and recorded meal intakes using the Renpho Smart Food Scale. After removal of days with <2 meals recorded, a total of 1132 meals over 365 days were available for analysis. To identify meal intakes, the proposed algorithm scans CGM data, identifies local maxima, and measures their prominence, i.e., the relative height with respect to a neighborhood window of length L. Peaks with prominence higher than Pmin are labeled as meals. Subject data was divided into a ~70% training set (n=31, 775 meals), where L and Pmin are tuned, and ~30% test set (n=12, 357 meals), where the algorithm is evaluated in terms of absolute percent error (APE) between number of recorded and detected meals.
Across subjects, mean±sd daily APE is 25.1±9.70%. Similar values hold when considering only subjects with <3.5 meals/day (n=6, 144 meals, APE=24.5±13.2%) and ≥3.5 meals/day (n=6, 213 meals, APE=25.5±6.93%), demonstrating the reliability of the algorithm to identify the number of meals in subjects with different dietary behaviors.
These results evidence the suitability of CGM for the automatic quantification of meal intakes. Further assessment of the algorithm should be performed using more controlled meal intake recordings.
Disclosure
N. Camerlingo: Employee; Pfizer Inc. A. Messere: None. M. Santamaria: None. C. Demanuele: None. D. Caouette: Employee; Pfizer Inc. K. C. Thomas: None. N. Shaafi kabiri: None. D. Psaltos: Employee; Pfizer Inc. F. Karahanoglu: None. S. Khan: None. I. Messina: Research Support; Pfizer Inc. M. Wicker: None. M. Kelly: Research Support; Pfizer Inc. H. Zhang: Employee; Pfizer Inc.
Accelerometry has become increasingly prevalent to monitor physical activity due to its low participant burden, quantitative metrics, and ease of deployment. Physical activity metrics are ideal for ...extracting intuitive, continuous measures of participants' health from multiple days or weeks of high frequency data due to their fairly straightforward computation. Previously, we released an open-source digital health python processing package, SciKit Digital Health (SKDH), with the goal of providing a unifying device-agnostic framework for multiple digital health algorithms, such as activity, gait, and sleep.
In this paper, we present the open-source SKDH implementation for the derivation of activity endpoints from accelerometer data. In this implementation, we include some non-typical features that have shown promise in providing additional context to activity patterns, and provide comparisons to existing algorithms, namely GGIR and the GENEActiv macros. Following this reference comparison, we investigate the association between age and derived physical activity metrics in a healthy adult cohort collected in the Pfizer Innovation Research Lab, comprising 7-14 days of at-home data collected from younger (18-40 years) and older (65-85 years) healthy volunteers.
Results showed that activity metrics derived with SKDH had moderate to excellent ICC values (
to
compared to GGIR,
to
compared to the GENEActiv macros), with high correlations for almost all compared metrics (>0.733 except vs GGIR sedentary time,
). Several features show age-group differences, with Cohen's
effect sizes >1.0 and
< 0.001. These features included non-threshold methods such as intensity gradient, and activity fragmentation features such as between-states transition probabilities.
These results demonstrate the validity of the implemented SKDH physical activity algorithm, and the potential of the implemented PA metrics in assessing activity changes in free-living conditions.
Abstract Schizophrenia is a complex psychiatric disorder and many of the factors contributing to its pathogenesis are poorly understood. In addition, identifying reliable neurophysiological markers ...would improve diagnosis and early identification of this disease. The 22q11.2 deletion syndrome (22q11DS) is one major risk factor for schizophrenia. Here, we show further evidence that deviant temporal dynamics of EEG microstates are a potential neurophysiological marker by showing that the resting state patterns of 22q11DS are similar to those found in schizophrenia patients. The EEG microstates are recurrent topographic distributions of the ongoing scalp potential fields with temporal stability of around 80 ms that are mapping the fast reconfiguration of resting state networks. Five minutes of high-density EEG recordings was analysed from 27 adult chronic schizophrenia patients, 27 adult controls, 30 adolescents with 22q11DS, and 28 adolescent controls. In both patient groups we found increased class C, but decreased class D presence and high transition probabilities towards the class C microstates. Moreover, these aberrant temporal dynamics in the two patient groups were also expressed by perturbations of the long-range dependency of the EEG microstates. These findings point to a deficient function of the salience and attention resting state networks in schizophrenia and 22q11DS as class C and class D microstates were previously associated with these networks, respectively. These findings elucidate similarities between individuals at risk and schizophrenia patients and support the notion that abnormal temporal patterns of EEG microstates might constitute a marker for developing schizophrenia.
Communication difficulties are a core deficit in many people with autism spectrum disorder (ASD). The current study evaluated neural activation in participants with ASD and neurotypical (NT) controls ...during a speech production task.
Neural activities of participants with ASD (N = 15, M = 16.7 years, language abilities ranged from low verbal abilities to verbally fluent) and NT controls (N = 12, M = 17.1 years) was examined using functional magnetic resonance imaging with a sparse-sampling paradigm.
There were no differences between the ASD and NT groups in average speech activation or inter-subject run-to-run variability in speech activation. Intra-subject run-to-run neural variability was greater in the ASD group and was positively correlated with autism severity in cortical areas associated with speech.
These findings highlight the importance of understanding intra-subject neural variability in participants with ASD.
•An fMRI speech production task was done with participants with ASD who ranged from low verbal abilities to verbally fluent.•There was no difference in speech activation in participants with ASD and neurotypical controls.•Participants with ASD had increased intra-subject variability in neural activity during speech compared to NT participants.•Increased intra-subject variability was associated with increased autism severity in the ASD group, measured via ADOS-CSS.
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